Skip to Main content Skip to Navigation
New interface
Journal articles

Joint Prediction of Continuous and Discrete States in Time-Series Based on Belief Functions.

Abstract : Forecasting the future states of a complex system is a complicated challenge that is encountered in many industrial applications covered in the community of Prognostics and Health Management (PHM). Practically, states can be either continuous or discrete: Continuous states generally represent the value of a signal while discrete states generally depict functioning modes reflecting the current degradation. For each case, specific techniques exist. In this paper, we propose an approach based on case-based reasoning that jointly estimates the future values of the continuous signal and the future discrete modes. The main characteristics of the proposed approach are the following: 1) It relies on the K-nearest neighbours algorithm based on belief functions theory; 2) Belief functions allow the user to represent his partial knowledge concerning the possible states in the training dataset, in particular concerning transitions between functioning modes which are imprecisely known; 3) Two distinct strategies are proposed for states prediction and the fusion of both strategies is also considered. Two real datasets were used in order to assess the performance in estimating future break-down of a real system.
Document type :
Journal articles
Complete list of metadata

Cited literature [39 references]  Display  Hide  Download
Contributor : Martine Azema Connect in order to contact the contributor
Submitted on : Monday, April 18, 2016 - 11:32:32 AM
Last modification on : Thursday, January 13, 2022 - 12:00:20 PM
Long-term archiving on: : Tuesday, November 15, 2016 - 5:27:26 AM


Files produced by the author(s)


  • HAL Id : hal-01303501, version 1


Emmanuel Ramasso, Michèle Rombaut, Noureddine Zerhouni. Joint Prediction of Continuous and Discrete States in Time-Series Based on Belief Functions.. IEEE Transactions on Cybernetics, 2013, 43 (1), pp.37-50. ⟨hal-01303501⟩



Record views


Files downloads